Marine mammals relying on tactile perception for hunting are able to achieve a remarkably high prey capture rate without visual or acoustic perception. Here, a self-powered triboelectric palm-like tactile sensor (TPTS) is designed to build a tactile perceptual system for underwater vehicles. It is enabled by a three-dimensional structure that mimics the leathery, granular texture in the palms of sea otters, whose inner neural architecture provides additional clues indicating the importance of tactile information. With the assistance of palm structure and triboelectric nanogenerator technology, the proposed TPTS has the ability to detect and distinguish normal and shear external load in real-time and approximate the external stimulation area, especially not affected by the touch frequency, that is, it can maintain stable performance under high-frequency contact. The results show that the TPTS is a promising tool for integration into grippers mounted on underwater vehicles to complete numerous underwater tasks.
With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in a ship microgrid. In order to balance the two non-compatible objectives, a new differential evolution variant, which is named as SaCIDE-r, was proposed to solve the optimization problem. In this algorithm, a Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-adaptive mechanism which was developed to avoid introducing extra control parameters. Further, to avoid being trapped in local optima, a re-initialization mechanism was developed. Then, we have evaluated the performances of the proposed SaCIDE-r approach by studying some numerical optimization problems of Congress on Evolutionary Computation (CEC) 2013 with D = 30, compared with seven stateof-the-art DE algorithms. Moreover, the proposed SaCIDE-r method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SaCIDE-r method might be a feasible solution for such a kind of optimization problem. INDEX TERMS Shipboard microgrid, global optimization, collective intelligence (CI), differential evolution (DE).
In this paper, a Self-learning Collective Intelligence Differential Evolution (SLCIDE) algorithm was proposed to optimize both the architecture and parameters of a Feedforward Neural Network (FNN). In order to improve the exploration-exploitation capability, a new Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-learning mechanism which was designed to adaptively select m top ranked donor vectors was developed by using a widely used unsupervised learning method, k-means. As a result, the proposed approach can be more adaptive and statistically powerful on versatile problems. Then, we evaluated the performances of the proposed SLCIDE approach by studying some numerical optimization problems of CEC 2013 with D = 30 and D = 50. Further, the proposed SLCIDE method was applied to train a FNN on four most popular datasets, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SLCIDE method obtain better results compared with other state-of-the-art algorithms. INDEX TERMS Evolutionary artificial neural network, global optimization, collective intelligence (CI), differential evolution (DE).
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